Expected Validation Performance and Estimation of a Random Variable's Maximum

Jesse Dodge*, Suchin Gururangan, Dallas Card, Roy Schwartz, Noah A. Smith

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we analyze three statistical estimators for expected validation performance, a tool used for reporting performance (e.g., accuracy) as a function of computational budget (e.g., number of hyperparameter tuning experiments). Where previous work analyzing such estimators focused on the bias, we also examine the variance and mean squared error (MSE). In both synthetic and realistic scenarios, we evaluate three estimators and find the unbiased estimator has the highest variance, and the estimator with the smallest variance has the largest bias; the estimator with the smallest MSE strikes a balance between bias and variance, displaying a classic bias-variance tradeoff. We use expected validation performance to compare between different models, and analyze how frequently each estimator leads to drawing incorrect conclusions about which of two models performs best. We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE.

Original languageAmerican English
Title of host publicationFindings of the Association for Computational Linguistics, Findings of ACL
Subtitle of host publicationEMNLP 2021
EditorsMarie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-Tau Yih
PublisherAssociation for Computational Linguistics (ACL)
Pages4066-4073
Number of pages8
ISBN (Electronic)9781955917100
StatePublished - 2021
Event2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021 - Punta Cana, Dominican Republic
Duration: 7 Nov 202111 Nov 2021

Publication series

NameFindings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021

Conference

Conference2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021
Country/TerritoryDominican Republic
CityPunta Cana
Period7/11/2111/11/21

Bibliographical note

Publisher Copyright:
© 2021 Association for Computational Linguistics.

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